Non-Pathological Psychological Distress among Mainland Chinese in Canada and Its Sociodemographic Risk Factors amidst the Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has exacerbated health inequalities, with a potentially heightened mental health risk for Mainland Chinese in Canada, given the rising anti-Chinese discrimination, and barriers in assessing health services. In this context, this study aimed to assess non-pathological psychological distress towards COVID-19 and identify its sociodemographic risk factors among Mainland Chinese in Canada at the early stages of the pandemic. Methods: A sample of 731 Mainland Chinese aged 16 or older completed an on-line survey to examine their attitudes, behavioural, and psychological responses towards COVID-19. Non-pathological psychological distress was assessed with a 7-item self-report scale to capture common emotional reactions towards COVID-19. Results: A factor analysis revealed a single-factor structure of the 7-item COVID-19 psychological distress scale (Eigen λ = 3.79). A composite psychological distress index (PDI) score was calculated from these items and used as the outcome variable. Multivariate regression models identified age, financial satisfaction, health status, and perceived/experienced discrimination as significant predictors of psychological distress (ps ≤ 0.05). Conclusions: Mainland Chinese in Canada who were over 25, in poor financial/health status, or with perceived/experienced discrimination were at a higher risk for COVID-19-related psychological distress. The health inequity across these factors would inform the services to mitigate mental health risk in minority groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".